Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Fix gelu in PyTorch frontend, tighten numerical checks #5763

Merged
merged 1 commit into from
Jun 11, 2020
Merged

Fix gelu in PyTorch frontend, tighten numerical checks #5763

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
20 changes: 12 additions & 8 deletions python/tvm/relay/frontend/pytorch.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -481,7 +481,10 @@ def _impl(inputs, input_types):
msg = "Data type %s could not be parsed in zeros op" % (type(data))
raise AssertionError(msg)

dtype = _convert_data_type(_convert_dtype_value(inputs[2]))
if inputs[2] is not None: # dtype given
dtype = _convert_data_type(_convert_dtype_value(inputs[2]))
else:
dtype = data.type_annotation.dtype

return _op.full(_expr.const(fill_value), shape, dtype=dtype)
return _impl
Expand Down Expand Up @@ -567,14 +570,13 @@ def _impl(inputs, input_types):

def _gelu():
def _impl(inputs, input_types):
import math
data = inputs[0]

def _pow3(x):
return x * x * x
return _expr.const(0.5) * data * (_expr.const(1.0) +
_op.tanh(_expr.const(math.sqrt(2.0 / math.pi)) *
(data + _expr.const(0.044715) * _pow3(data))))
# gelu is data * normcdf(data)
# normcdf expressed as erf because we don't currently have that intrinsic
# note that there is also a fastgelu variant approximating normcdf
# with tanh and third order polynomials, but this is "true" gelu
return data * (_expr.const(0.5) +
_op.erf(data * _expr.const(0.5**0.5)) * _expr.const(0.5))
return _impl

def _selu():
Expand Down Expand Up @@ -1832,6 +1834,7 @@ def _get_convert_map(prelude):
"aten::Int" : _int(),
"prim::NumToTensor" : _numtotensor(),
"prim::ImplicitTensorToNum" : _tensortonum(),
"aten::ScalarImplicit" : _tensortonum(),
"aten::constant_pad_nd" : _pad("constant"),
"aten::reflection_pad1d" : _pad("reflect"),
"aten::reflection_pad2d" : _pad("reflect"),
Expand Down Expand Up @@ -1870,6 +1873,7 @@ def _get_convert_map(prelude):
"aten::floor" : _unary("floor"),
"aten::round" : _unary("round"),
"aten::isfinite" : _unary("isfinite"),
"aten::isinf" : _unary("isinf"),
"aten::isnan" : _unary("isnan"),
"aten::clamp" : _clamp(),
"aten::detach" : _identity(),
Expand Down
27 changes: 14 additions & 13 deletions tests/python/frontend/pytorch/test_forward.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -135,7 +135,8 @@ def measure_latency(model, input_shapes, output_shapes, thresh, dryruns=40):

def verify_model(model_name, input_data=[],
custom_convert_map={},
ctx_list=ctx_list()):
ctx_list=ctx_list(),
rtol=1e-5, atol=1e-5):
"""Assert that the output of a compiled model matches with that of its
baseline."""
if isinstance(model_name, str):
Expand Down Expand Up @@ -190,7 +191,7 @@ def verify_model(model_name, input_data=[],

assert_shapes_match(baseline_output, compiled_output)
tvm.testing.assert_allclose(baseline_output, compiled_output,
rtol=1e-3, atol=1e-3)
rtol=rtol, atol=atol)

del model_name
del baseline_model
Expand Down Expand Up @@ -1189,35 +1190,35 @@ def test_conv3d():
# Model tests
def test_resnet18():
torch.set_grad_enabled(False)
verify_model("resnet18")
verify_model("resnet18", atol=1e-4, rtol=1e-4)

def test_squeezenet1_0():
torch.set_grad_enabled(False)
verify_model("squeezenet1_0")
verify_model("squeezenet1_0", atol=1e-4, rtol=1e-4)

def test_squeezenet1_1():
torch.set_grad_enabled(False)
verify_model("squeezenet1_1")
verify_model("squeezenet1_1", atol=1e-4, rtol=1e-4)

def test_densenet121():
torch.set_grad_enabled(False)
verify_model("densenet121")
verify_model("densenet121", atol=1e-4, rtol=1e-4)

def test_inception_v3():
torch.set_grad_enabled(False)
verify_model("inception_v3")
verify_model("inception_v3", atol=1e-4, rtol=1e-4)

def test_googlenet():
torch.set_grad_enabled(False)
verify_model("googlenet")
verify_model("googlenet", atol=1e-4, rtol=1e-4)

def test_mnasnet0_5():
torch.set_grad_enabled(False)
verify_model("mnasnet0_5")
verify_model("mnasnet0_5", atol=1e-4, rtol=1e-4)

def test_mobilenet_v2():
torch.set_grad_enabled(False)
verify_model("mobilenet_v2")
verify_model("mobilenet_v2", atol=1e-4, rtol=1e-4)

"""
#TODO: Fix VGG and AlexNet issues (probably due to pooling)
Expand Down Expand Up @@ -1278,19 +1279,19 @@ def forward(self, inp):

inp = [torch.rand((1, 3, 300, 300), dtype=torch.float)]

verify_model(SegmentationModelWrapper(fcn.eval()), inp)
verify_model(SegmentationModelWrapper(fcn.eval()), inp, atol=1e-4, rtol=1e-4)

# depthwise + dilated covolution not supported on x86
# see https://github.com/apache/incubator-tvm/issues/4962
cuda_ctx = ("cuda", tvm.gpu(0))
if cuda_ctx[1].exist:
verify_model(SegmentationModelWrapper(deeplab.eval()), inp, [cuda_ctx])
verify_model(SegmentationModelWrapper(deeplab.eval()), inp, [cuda_ctx], atol=1e-4, rtol=1e-4)


def test_3d_models():
input_shape = (1, 3, 4, 56, 56)
resnet3d = torchvision.models.video.r3d_18(pretrained=True).eval()
verify_model(resnet3d, [torch.rand(input_shape)])
verify_model(resnet3d, [torch.rand(input_shape)], atol=1e-4, rtol=1e-4)


def verify_script_model(pt_model, ishapes):
Expand Down